Synthetic air data output generation

ABSTRACT

In one example, a method includes receiving, over an aircraft data communications bus, a plurality of non-pneumatic inputs corresponding to aircraft operational parameters. The method further includes processing the plurality of non-pneumatic inputs through an artificial intelligence network to generate an air data output value, and outputting the air data output value to a consuming system for use when a pneumatic-based air data output value is determined to be unreliable.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application is a continuation of U.S. application Ser. No.14/962,137 filed Dec. 8, 2015 for “SYNTHETIC AIR DATA OUTPUT GENERATION”by Kaare Josef Anderson, Brian Daniel Matheis, Derrick D Hongerholt, andWilliam Kunik.

BACKGROUND

The present disclosure relates generally to air data systems, and moreparticularly to air data systems that can utilize artificialintelligence to generate air data outputs for an aircraft.

Modern aircraft often incorporate air data systems that calculate airdata outputs based on measured parameters collected from various sensorspositioned about the aircraft. For instance, many modern aircraftutilize pneumatic air data probes that measure pitot pressure, staticpressure, or other parameters of airflow across the probe. Suchpneumatic air data probes often include one or more air data sensingports, such as static pressure ports and/or total pressure (i.e.,stagnation pressure) ports. A portion of air flowing over the probes isdiverted to the ports that are pneumatically connected to pressuresensors that sense the atmospheric pressure outside the aircraft. Suchmeasured pressures are usable for determining air data outputs, such asaircraft pressure altitude, altitude rate (e.g., vertical speed),airspeed, Mach number, angle of attack, angle of sideslip, or other airdata outputs.

To increase system reliability, aircraft manufacturers typicallyincorporate redundant (e.g., backup) systems that can provide outputs toconsuming systems in the event that a primary system fails or isotherwise determined to be unreliable. For instance, many aircraftincorporate multiple (e.g., two, three, four, or more) pneumatic airdata probes, certain of which are designated as backup systems for usewhen a primary system is deemed unreliable.

SUMMARY

In one example, a method includes receiving, over an aircraft datacommunications bus, a plurality of non-pneumatic inputs corresponding toaircraft operational parameters. The method further includes processingthe plurality of non-pneumatic inputs through an artificial intelligencenetwork to generate an air data output value, and outputting the airdata output value to a consuming system for use when a pneumatic-basedair data output value is determined to be unreliable.

In another example, a synthetic air data system includes at least oneprocessor and computer-readable memory. The computer-readable memory isencoded with instructions that, when executed by the at least oneprocessor, cause the synthetic air data system to receive, over anaircraft data communications bus, a plurality of non-pneumatic inputscorresponding to aircraft operational parameters. The computer readablememory is further encoded with instructions that, when executed by theat least one processor, cause the synthetic air data system to processthe plurality of non-pneumatic inputs through an artificial intelligencenetwork to generate an air data output value, and output the air dataoutput value to a consuming system for use when a pneumatic-based airdata output value is determined to be unreliable.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic block diagram of an example aircraft including asynthetic air data system that can process non-pneumatic inputs throughan artificial intelligence network to generate one or more air dataoutput values.

FIG. 2 is a schematic diagram of an example artificial neural networkthat can be used to process non-pneumatic inputs to generate one or moreair data output values.

FIG. 3 is a flow diagram illustrating example operations to processnon-pneumatic inputs through an artificial intelligence network togenerate one or more air data output values.

DETAILED DESCRIPTION

As described herein, a synthetic air data system can process a pluralityof non-pneumatic inputs through an artificial intelligence network togenerate one or more air data output values. Such non-pneumatic inputscan include, among others, aircraft thrust parameters, aircraft enginethrottle settings, flight control surface positions and/or surfaceloading parameters, aircraft remaining fuel weight and/or usage rates,aircraft weight, landing gear position (e.g., deployed or stowed),aircraft mass balance, and aircraft acceleration and/or angular rates(e.g., received from an inertial reference system). The artificialintelligence network, such as an artificial neural network, cancorrelate the received inputs to one or more air data output values,such as airspeed, altitude, Mach number, angle of attack, angle ofsideslip, or other air data output values. As such, a synthetic air datasystem implementing techniques of this disclosure can generate air dataoutput values via a system that is dissimilar in design to traditionaldirect-measurement systems (e.g., pneumatic-based, optical, ultrasonic,or other sensor-based systems that directly measure the air data value)and that can be used, e.g., when a sensor-based air data output value,such as a pneumatic-based air data output value, is determined to beunreliable. Moreover, such air data output values can be generated frommeasured inputs that are provided by existing aircraft systems, therebydecreasing the time and cost required to install additional sensors orhardware components on new and existing aircraft platforms whenincorporating the synthetic air data system.

FIG. 1 is a schematic block diagram of aircraft 10 including syntheticair data system 12 that can process non-pneumatic inputs through anartificial intelligence network to generate one or more air data outputvalues. As illustrated in FIG. 1, aircraft 10 can further includepneumatic air data probe 14A and pneumatic air data probe 14B(collectively referred to herein as “pneumatic air data probes 14”), airdata computer (ADC) 16A and air data computer 16B (collectively referredto herein as “air data computers 16”), producing systems 18, consumingsystems 20, and data concentrator unit (DCU) 22.

Pneumatic air data probes 14 are positioned at an exterior of aircraft10 to sense one or more pressures of air flowing over the probes.Pneumatic air data probes 14 include one or more air data sensing ports(not illustrated) to which airflow around pneumatic air data probes 14is diverted. The air data sensing ports are pneumatically connected topressure sensors (e.g., pressure transducers or other pressure sensors)that measure the collected airflows to generate measured pressures thatare usable in determining, e.g., static pressure, total pressure (i.e.,stagnation pressure), or other pressures of the airflow around aircraft10. Outputs of the pressure sensors are electrically connected to airdata computers 16, which generate air data output values based on thereceived pneumatic pressures.

As illustrated in FIG. 1, air data computer 16A is adjacent pneumaticair data probe 14A and air data computer 16B is adjacent pneumatic airdata probe 14B. In other examples, air data computers 16 need not beadjacent air data probes 14. For instance, air data computers 16 can belocated within the interior of aircraft 10 at a location that is remotefrom pneumatic air data probes 14, such as within an electronics bay ofaircraft 10. In addition, while illustrated as including two pneumaticair data probes 14 and two corresponding air data computers 16, aspectsof this disclosure are not so limited. For instance, in other examples,aircraft 10 can include more or fewer than two of each of pneumatic airdata probes 14 and air data computers 16, and the number of air datacomputers 16 need not match the number of pneumatic air data probes 14.In general, aircraft 10 includes one or more air data computers 16 thatare electrically and/or communicatively coupled with one or more airdata probes 14 to receive indications of measured pneumatic pressures(e.g., static pressure and total pressure) of airflow around theexterior of aircraft 10 sensed by the one or more pneumatic air dataprobes 14.

Air data computers 16 house electrical components, such as one or moreprocessors, computer-readable memory, or other electrical componentsconfigured to generate air data outputs corresponding to one or moreoperational states of aircraft 10. Non-limiting examples of such airdata outputs include calibrated airspeed, true airspeed, Mach number,altitude (e.g., pressure altitude), angle of attack (i.e., an anglebetween oncoming airflow or relative wind and a reference line of a wingof aircraft 10), vertical speed (e.g., altitude rate), and angle ofsideslip (i.e., an angle between a direction of travel and a directionextending through a nose of aircraft 10). Accordingly, air data outputsgenerated by air data computers 16 based on pneumatic pressures receivedby pneumatic air data probes 14 can be considered to be pneumatic-basedair data outputs.

As further illustrated in FIG. 1, aircraft 10 includes producing systems18. Producing systems 18 include operational systems of aircraft 10 thatproduce non-pneumatic outputs usable by synthetic air data system 12 asinputs to generate air data output values, as is further describedbelow. For example, producing systems 18 can include aircraft enginesand/or thrust control systems, aircraft fuel systems, flight managementcontrol systems, aircraft navigational systems such as inertialreference systems (IRS), attitude heading and reference systems (AHARS),global positioning system (GPS) and/or satellite information systems,landing gear systems, or other operational systems of aircraft 10.Producing systems 18, as illustrated, are communicatively coupled withdata concentrator unit (DCU) 22.

Data concentrator unit 22 is an electronic device comprising one or moreprocessors, computer-readable memory, and data transceivers configuredto receive digital and/or analog signals from various aircraft systemsand format the received signals for transmission according to a definedcommunications protocol, such as the protocol defined by theAeronautical Radio, Incorporated (ARINC) 429 standard. For instance, asillustrated in FIG. 1, data concentrator unit 22 can receive inputs fromproducing systems 18 and can transmit the inputs over communicationsdata bus 24 for receipt by one or more aircraft systems, such assynthetic air data system 12, air data computers 16, consuming systems20, or other systems of aircraft 10. Communications data bus 24 can beany data bus that communicatively couples components of aircraft 10 andenables communication between the interconnected components via adefined communications protocol (e.g., ARINC 429).

Consuming systems 20 can be any operational system of aircraft 10configured to receive air data output values from air data computers 16and/or synthetic air data system 12 for use during operation of aircraft10. For instance, consuming systems 20 can include any one or more offlight management systems, automatic flight control systems, aircraftdisplay systems (e.g., primary flight displays, multifunction displays,control display units, or other display systems), or other operationalsystems of aircraft 10 that can utilize the received air data outputvalues during operation of aircraft 10. In some examples, certainaircraft systems can be included in both producing systems 18 andconsuming systems 20. For instance, a flight management computer can beincluded as one of producing systems 18 that outputs a calculatedaircraft mass balance, remaining fuel, fuel usage rate, aircraftaltitude, or other non-pneumatic outputs that are utilized by syntheticair data system 12 for generation of one or more air data output values.In addition, the flight management computer can be included as one ofconsuming systems 20 that receives generated air data output values fromsynthetic air data system 12 for use when a pneumatic-based air dataoutput value determined by, e.g., one or more of air data computers 16is determined to be unreliable. Accordingly, producing systems 18 andconsuming systems 20 can each include any one or more aircraft systems,and the respective systems need not be unique to either of producingsystems 18 and consuming systems 20.

As illustrated in FIG. 1, synthetic air data system 12 iscommunicatively connected to air data computers 16, consuming systems20, and data concentrator unit 22 via communications data bus 24.However, while in the example of FIG. 1 synthetic air data system 12 iscommunicatively connected to producing systems 18 via data concentratorunit 22, in other examples, synthetic air data system 12 can be directlyconnected (e.g., communicatively and/or electrically connected) to anyone or more of producing systems 18.

Synthetic air data system 12 can include one or more processors andcomputer readable memory encoded with instructions that, when executedby the one or more processors, cause synthetic air data system 12 tooperate in accordance with techniques described herein. Synthetic airdata system 12, in some examples, can include one or more stand-aloneelectronic devices, such that synthetic air data system is separate fromair data computers 16 and each of consuming systems 20. In otherexamples, synthetic air data system 12 can be included in any one ormore of air data computers 16 and/or consuming systems 20, such thatfunctionality attributed herein to synthetic air data system 12 isperformed by and/or distributed among one or more electronic devices ofsuch other systems. For instance, in some examples, any one or more ofair data computers 16 can implement functionality attributed herein tosynthetic air data system 12. In general, synthetic air data system 12includes one or more processors and computer readable memory encodedwith instructions that, when executed by the one or more processors,cause synthetic air data system 12 to process received non-pneumaticinputs through an artificial intelligence network to generate an airdata output value.

Examples of one or more processors of synthetic air data system 12 caninclude any one or more of a microprocessor, a controller (e.g.,microcontroller), a digital signal processor (DSP), an applicationspecific integrated circuit (ASIC), a field-programmable gate array(FPGA), or other equivalent discrete or integrated logic circuitry.Computer readable memory of synthetic air data system 12 can beconfigured to store information within synthetic air data system 12during operation. Such computer-readable memory, in some examples, isdescribed as computer-readable storage media. In some examples, acomputer-readable storage medium can include a non-transitory medium.The term “non-transitory” can indicate that the storage medium is notembodied in a carrier wave or a propagated signal. In certain examples,a non-transitory storage medium can store data that can, over time,change (e.g., in RAM or cache). In some examples, the computer-readablememory is a temporary memory, meaning that a primary purpose of thecomputer-readable memory is not long-term storage. Computer-readablememory, in some examples, includes and/or is described as volatilememory, meaning that the computer-readable memory does not maintainstored contents when power to synthetic air data system 12 is removed.Examples of volatile memories can include random access memories (RAM),dynamic random access memories (DRAM), static random access memories(SRAM), and other forms of volatile memories. In some examples,computer-readable memory is used to store program instructions forexecution by one or more processors of synthetic air data system 12.Computer-readable memory, in one example, is used by software orapplications executing on synthetic air data system 12 to temporarilystore information during program execution.

Computer-readable memory of synthetic air data system 12, in someexamples, also includes one or more computer-readable storage media.Computer-readable storage media can be configured to store largeramounts of information than volatile memory. Computer-readable storagemedia can be configured for long-term storage of information. In someexamples, computer-readable storage media include non-volatile storageelements. Examples of such non-volatile storage elements can includemagnetic hard discs, optical discs, floppy discs, flash memories, orforms of electrically programmable memories (EPROM) or electricallyerasable and programmable (EEPROM) memories.

In operation, synthetic air data system 12 receives, over communicationsdata bus 24, non-pneumatic inputs corresponding to aircraft operationalparameters. For instance, in the example of FIG. 1, synthetic air datasystem 12 can receive a plurality of non-pneumatic inputs from producingsystems 18 via data concentrator unit 22 and communications data bus 24.Examples of such non-pneumatic inputs can include, but are not limitedto, inputs corresponding to aircraft control surface position (e.g.,ailerons, elevator, rudder, spoilerons, flaps, slats, or other controlsurfaces) and/or control surface loading, aircraft mass and/or massbalance (e.g., current and/or at a predefined time, such as at takeoff),remaining fuel weight, engine thrust parameters (e.g., engine N1, N2,EGT, throttle settings, or other thrust parameters), aircraftnavigational information (e.g., aircraft position, heading, altitude,ground speed, airspeed, or other navigational information), airtemperature information (e.g., static air temperature, total airtemperature, outside air temperature, or other temperature information),aircraft acceleration and/or angular rate information (e.g., receivedfrom an IRS), landing gear position information (e.g., deployed, stowed,in transit, or other landing gear position information), or othernon-pneumatic inputs. In general, non-pneumatic inputs can include anyinput indicative of an aircraft operational state received from anon-pneumatic source (e.g., sources other than pneumatic air data probes14).

Synthetic air data system 12 processes the plurality of non-pneumaticinputs through an artificial intelligence network to generate one ormore air data output values (e.g., calibrated airspeed, true airspeed,Mach number, pressure altitude, angle of attack, angle of sideslip, orother air data output values). Examples of such artificial intelligencenetworks include artificial neural networks, probabilistic graphicalmodels such as Bayesian networks, probabilistic classifiers and/orcontrollers (e.g., Gaussian mixture models), or other forms ofartificial intelligence networks. As one example, the artificialintelligence network can be an artificial neural network having at leastone internal layer of nodes (often referred to as a hidden layer ofneurons) that apply one or more weights, biases, and/or transferfunctions to the plurality of non-pneumatic inputs to correlate theplurality of non-pneumatic inputs to one or more air data output values.

In some examples, the artificial intelligence network can be pre-trainedbased on previously-obtained data (e.g., flight test data) to correlatethe plurality of non-pneumatic inputs to the one or more air data outputvalues. In certain examples, synthetic air data system 12 can utilize asingle artificial intelligence network to generate a plurality of airdata output values from a plurality of non-pneumatic inputs. In otherexamples, synthetic air data system 12 can utilize multiple, separateartificial intelligence networks that each correlate a particular set ofnon-pneumatic inputs to a selected air data output value. For instance,synthetic air data system 12 can utilize a first artificial intelligencenetwork that correlates a first set of non-pneumatic inputs to a firstair data output value (e.g., angle of attack), and can utilize a secondartificial intelligence network that correlates a second set ofnon-pneumatic inputs to a second air data output value (e.g., angle ofsideslip). The first and second sets of non-pneumatic inputs can be thesame or difference sets of inputs.

In certain examples, such as when the artificial intelligence network isan artificial neural network, the weights, biases, and transferfunctions of the hidden layer of neurons can be pre-defined (e.g., viaoffline pre-training) and fixed, such that synthetic air data system 12does not modify the weights, biases, and transfer functions duringoperation of synthetic air data system 12. In other examples, syntheticair data system 12 can incorporate an active training (or “learning”)mode in which synthetic air data system 12 modifies the weights, biases,and transfer functions applied by the neurons based on feedback of thegenerated air data output and a reference value, such as apneumatic-based air data output value. That is, in certain examples,synthetic air data system 12 can receive as input one or morepneumatic-based air data outputs generated by, e.g., air data computers16 via measured pressures received from pneumatic air data probes 14. Insuch examples, synthetic air data system 12 can effectively train theartificial intelligence network based on non-pneumatic inputs receivedfrom producing systems 18 and pneumatic-based air data outputs generatedby, e.g., air data computers 16.

In certain examples, synthetic air data system 12 can identify whetherthe received pneumatic-based air data output value is determined to bereliable. For instance, synthetic air data system 12 can receive astatus indication or other indication of reliability of thepneumatic-based air data output value from e.g., air data computers 16or one or more of consuming systems 20, such as a flight managementcomputer, automatic flight control system, or other of consuming systems20. In other examples, synthetic air data system 12 can determinewhether the received pneumatic-based air data output value is reliable,such as by comparing pneumatic-based air data output values receivedfrom multiple sources (e.g., multiple of air data computers 16) to eachother or to a threshold value. Synthetic air data system 12 can processthe non-pneumatic inputs and the received pneumatic-based air dataoutput through the artificial intelligence network to generate the airdata output value (and optionally train the artificial neural network)when the pneumatic-based air data output value is determined to bereliable. Synthetic air data system 12 can process the non-pneumaticinputs alone (i.e., without processing the received pneumatic-based airdata output value) through the artificial intelligence network togenerate the air data output value when the received pneumatic-based airdata output value is determined to be unreliable.

Such active training can enable synthetic air data system 12 to maintainand/or initialize dynamic internal states of the artificial neuralnetwork. In addition, active training and/or comparison of the air dataoutputs generated by synthetic air data system 12 to receivedpneumatic-based air data outputs can enable synthetic air data system 12to determine an estimated error of the air data outputs generated bysynthetic air data system 12. In certain examples, synthetic air datasystem 12 can generate an indication of reliability of the air dataoutputs generated by synthetic air data system 12 based on thecomparison.

Synthetic air data system 12 can output the generated air data outputvalue (i.e., generated by synthetic air data system 12) to one or moreof consuming systems 20 (e.g., via communications data bus 24) for usewhen a pneumatic-based air data output value is determined to beunreliable. In some examples, synthetic air data system 12 can determinewhether the pneumatic-based air data output value is unreliable, such asby comparing received pneumatic-based air data outputs to each otherand/or to a threshold deviation parameter. In other examples, one ormore of consuming systems 20 (e.g., a flight management system) candetermine the reliability of the pneumatic-based air data output value,and can designate the air data output value generated by synthetic airdata system 12 for use when the pneumatic-based air data output value isdetermined to be unreliable.

In certain examples, the air data output value generated by syntheticair data system 12 can be utilized to determine whether thepneumatic-based air data output value is reliable. For instance,synthetic air data system 12 and/or one or more of consuming systems 20can compare pneumatic-based air data outputs to the air data output(s)generated by synthetic air data system 12. A pneumatic-based air dataoutput can be determined to be unreliable when, e.g., a correspondingpneumatic-based air data output received from a first one of air datacomputers 16 is within a threshold deviation from the air data outputgenerated by synthetic air data system 12 and the pneumatic-based airdata output received from a second one of air data computers 16 exceedsthe threshold deviation from the air data output generated by syntheticair data system 12. In such an example, the pneumatic-based air dataoutput received from the first one of air data computers 16 (that iswithin the threshold deviation from the air data output generated bysynthetic air data system 12) can be determined to be reliable. Thepneumatic-based air data output received from the second one of air datacomputers 16 (that exceeds the threshold deviation) can be determined tobe unreliable.

Consuming systems 20 can utilize one or more air data outputs generatedby synthetic air data system 12 for operation when correspondingpneumatic-based air data outputs are determined to be unreliable. Assuch, synthetic air data system 12 can provide a redundant (e.g.,backup) air data system that generates air data output values usable foroperation of aircraft 12 when one or more pneumatic-based air dataoutput values are determined to be unreliable. The air data outputvalues generated by synthetic air data system 12 can be based onnon-pneumatic source inputs, thereby providing an air data system thatis dissimilar in design to the pneumatic-based air data systems andenhancing operational reliability of aircraft 10. Moreover, thenon-pneumatic inputs received and processed by synthetic air data system12 can be selected from inputs available on new and existing aircraftplatforms, thereby reducing the time and cost required to incorporatesynthetic air data system 12 into such aircraft designs.

FIG. 2 is a schematic diagram of an example artificial neural network 26that can be used to process non-pneumatic inputs to generate one or moreair data output values. For purposes of clarity and ease of discussion,the example artificial neural network 26 of FIG. 2 is described belowwithin the context of aircraft 10 including synthetic air data system 12of FIG. 1.

As illustrated in FIG. 2, artificial neural network 26 includes inputnodes 28A-28N (collectively referred to herein as “inputs 28”), internalnodes (or neurons) 30A-30M (collectively referred to herein as “neurons30” and often referred to as a hidden layer), and output node 32. Itshould be understood that in the example of FIG. 2, the letter “N” ofinput node 28N and the letter “M” of internal node 30M representarbitrary numbers, such that each of inputs 28 and neurons 30 caninclude any number of input nodes and internal nodes, respectively. Incertain examples, artificial neural network 26 includes a number ofneurons 30 that is one less than the number of inputs 28. That is, whilethe letter “N” of input node 28N represents an arbitrary number, in someexamples, the letter “M” of internal node “30M” represents a number thatis one less than the arbitrary number represented by the letter “N”.

Each of inputs 28 corresponds to one of the plurality of non-pneumaticinputs received from producing systems 18, though in examples wheresynthetic air data system 12 processes pneumatic-based air data outputsreceived from air data computers 16, certain of inputs 28 can correspondto the received pneumatic-based air data outputs. Each of neurons 30applies a weight, bias, and transfer function (e.g., a sigmoid function)to each of inputs 28 to generate intermediate outputs provided byneurons 30. In the illustrated example of FIG. 2, the intermediateoutputs provided by neurons 30 are provided as inputs to output node 32.Output node 32 applies predetermined weights, biases, and/or a transferfunction to the intermediate outputs to generate a particular air dataoutput value (e.g., calibrated airspeed, true airspeed, Mach number,pressure altitude, angle of attack, angle of sideslip, or other air dataoutput values).

In the example of FIG. 2, artificial neural network 26 processes inputs28 to generate a single air data output value at output node 32.However, in other examples, artificial neural network 26 can processinputs 28 to determine multiple air data outputs (i.e., at multipleoutput nodes). In certain examples, artificial neural network 26 canrepresent a first artificial neural network that correlates a first setof inputs (e.g., inputs 28) to a first air data output value (e.g., atoutput node 32). In such examples, synthetic air data system 12 (ofFIG. 1) can utilize a second artificial neural network that correlates asecond set of inputs to a second, different air data output value byutilizing different weights, biases, and transfer functions at neurons30. The first set of inputs (e.g., inputs 28) can be the same ordifferent than the second set of inputs.

As an example, synthetic air data system 12 can utilize artificialneural network 26 that generates a first air data output value (e.g.,angle of attack) at output node 32 using a first set of non-pneumaticinputs corresponding to inputs 28 and a first set of weights, biases,and transfer functions at neurons 30. Synthetic air data system 12 canutilize a second artificial neural network (e.g., of the samearchitectural form of neural network 26) that generates a second airdata output value (e.g., angle of sideslip) at the output node using asecond set of non-pneumatic inputs and a second set of weights, biases,and transfer functions at the hidden layer of neurons. The second set ofnon-pneumatic inputs (utilized to generate an angle of sideslip air dataoutput value) can be the same set of non-pneumatic inputs as the firstset of non-pneumatic inputs or a different set of non-pneumatic inputs.

In some examples, synthetic air data system 12 can store multipleartificial neural networks that are each usable to generate a samecategory of air data output value (e.g., angle of sideslip, angle ofattack, calibrated airspeed, or other categories of air data outputvalue). The multiple artificial neural networks can utilize differentsets of inputs and different weights, biases, and transfer functions togenerate the same category of air data output value. Synthetic air datasystem 12, in such examples, can select which of the multiple artificialneural networks to utilize to generate the category of air data outputvalue based on an availability and/or determined reliability of inputsto the multiple artificial neural networks. For instance, synthetic airdata system 12 can receive and/or determine a reliability and/oraccuracy status of each of the inputs to each of the multiple artificialneural networks. Synthetic air data system 12 can select, e.g., a firstof the multiple artificial neural networks for use in generating thecategory of air data output value. In the event that one or more of theinputs to the selected first of the multiple artificial neural networksis determined to be unreliable (or inaccurate) and each of the inputs toa second of the artificial neural networks is determined to be reliable(and accurate), synthetic air data system 12 can select the second ofthe multiple artificial neural networks for use in generating thecategory of air data output value. In this way, synthetic air datasystem 12 can increase robustness of air data output generation byenabling an air data output value to be generated based on any ofmultiple, different sets of non-pneumatic inputs.

While the example artificial neural network 26 of FIG. 2 is illustratedas a feed-forward neural network including a single hidden layer ofneurons 30, in some examples, artificial neural network 26 can take theform of a recurrent neural network in which connections between units(e.g., inputs 28, neurons 30, and/or output node 32) form a directedcycle that enables artificial neural network 26 to store internal statesof each of the nodes to thereby model dynamic temporal behavior. Inaddition, in some examples, artificial neural network 26 can include twoor more layers of neurons 30.

As described herein, artificial neural network 26, implemented bysynthetic air data system 12, can be used to generate one or more airdata output values based on non-pneumatic inputs received from variousproducing systems of an aircraft. The use of non-pneumatic inputs canprovide air data output values that are usable during operation of theaircraft (e.g., for controlled flight) and that are generated via asystem that is dissimilar in design to pneumatic-based air data systems.Accordingly, the use of air data output values generated by syntheticair data system 12 via artificial neural network 26 can help to increasethe operational reliability of the aircraft by increasing the chancethat an environmental or other condition that may cause anomalousbehavior of the pneumatic-based air data system does not adverselyaffect synthetic air data system 12.

FIG. 3 is a flow diagram illustrating example operations to processnon-pneumatic inputs through an artificial intelligence network togenerate one or more air data output values. For purposes of clarity andease of discussion, the example operations are described below withinthe context of aircraft 10 of FIG. 1 and artificial neural network 26 ofFIG. 2.

A plurality of non-pneumatic inputs can be received (step 34). Forexample, synthetic air data system 12 can receive a plurality ofnon-pneumatic inputs generated by producing systems 18 via dataconcentrator unit 22 and over communications data bus 24. It can bedetermined whether active training of an artificial intelligence networkis enabled (step 36). For instance, synthetic air data system 12 candetermine whether an active training mode of artificial neural network26 is enabled. In examples where the active training is enabled (“YES”branch of step 36), parameters of the artificial intelligence networkcan be modified based on, e.g., feedback values of generated air dataoutput values and/or a received reference value, such as a correspondingpneumatic-based air data output value (step 38). In examples where theactive training is not enabled (“NO” branch of step 36), the step ofmodifying the parameters of the artificial intelligence network can beomitted (or skipped).

The plurality of non-pneumatic inputs can be processed through theartificial intelligence network to generate an air data output value(step 40). For example, synthetic air data system 12 can process aplurality of non-pneumatic inputs through artificial neural network 26to generate an air data output value at output node 32.

It can be determined whether a pneumatic-based air data output value isunreliable (step 42). For instance, any one or more of consuming systems20 can determine whether a pneumatic-based air data output valuegenerated by air data computers 16 is reliable, or whether thepneumatic-based air data output value is unreliable. In examples wherethe pneumatic-based air data output value is not determined to beunreliable (“NO” branch of step 42), the pneumatic-based air data outputvalue can be utilized for operation, such as for controlled flight ofaircraft 10 (step 44). In examples where the pneumatic-based air dataoutput value is determined to be unreliable (“YES” branch of step 42),the air data output value generated by synthetic air data system 12 canbe utilized for operation, such as for controlled flight of aircraft 10(step 46). For instance, in certain examples, air data output valuesgenerated by one or more different (e.g., primary) system(s) can beutilized for flight, and the air data output value generated bysynthetic air data system 12 can be used as a backup air data outputvalue in response to a determination that the air data output valuesgenerated by the one or more primary systems are unreliable.

While the example operations described above include step 36 in which itis determined whether active training of the artificial intelligencenetwork is enabled, other example operations may not include step 36.For instance, as when the artificial neural network is adapted such thatactive training is unavailable, synthetic air data system 12 may notactively determine whether active training is enabled. Rather, syntheticair data system 12 may proceed directly to step 40 in response toreceiving the plurality of non-pneumatic inputs. Similarly, in exampleswhere the artificial neural network is adapted such that active trainingis always enabled, synthetic air data system 12 may proceed directly tostep 38 in response to receiving the plurality of non-pneumatic inputswithout actively determining whether active training is enabled.

According to techniques of this disclosure, a synthetic air data systemcan process a plurality of non-pneumatic inputs corresponding toaircraft operational parameters through an artificial intelligencenetwork to generate one or more air data output values. The syntheticair data system can output the one or more air data output values foruse when, e.g., a pneumatic-based air data output value is determined tobe unreliable. Accordingly, a synthetic air data system as describedherein can provide a source of generated air data output values forconsuming systems that is dissimilar in design to traditionalpneumatic-based air data systems, thereby helping to enhance aircraftoperational reliability.

The following are non-exclusive descriptions of possible embodiments ofthe present invention.

A method includes receiving, over an aircraft data communications bus, aplurality of non-pneumatic inputs corresponding to aircraft operationalparameters. The method further includes processing the plurality ofnon-pneumatic inputs through an artificial intelligence network togenerate an air data output value, and outputting the air data outputvalue to a consuming system for use when a pneumatic-based air dataoutput value is determined to be unreliable.

The method of the preceding paragraph can optionally include,additionally and/or alternatively, any one or more of the followingfeatures, configurations, operations, and/or additional components.

The plurality of non-pneumatic inputs can include one or more of anaircraft engine thrust parameter, an aircraft engine throttle setting, aflight control surface position, a flight control surface loading, anaircraft fuel usage rate, an aircraft weight, a landing gear position,an aircraft mass balance, an aircraft acceleration, and an aircraftangular rate.

The generated air data output value can be selected from a groupincluding an aircraft calibrated airspeed, an aircraft true airspeed, anaircraft Mach number, an aircraft pressure altitude, an aircraft angleof attack, an aircraft vertical speed, and an aircraft angle ofsideslip.

The artificial intelligence network can include an artificial neuralnetwork having at least one internal layer of neurons that apply one ormore weights, biases, or transfer functions to each of the plurality ofnon-pneumatic inputs to generate the air data output value.

The artificial neural network can be a feed-forward neural network.

The artificial neural network can be pre-trained to determine the one ormore weights, biases, or transfer functions.

Processing the plurality of non-pneumatic inputs through the artificialintelligence network to generate the air data output value can includeprocessing the plurality of non-pneumatic inputs through the artificialneural network without changing the one or more weights, biases, ortransfer functions.

The pre-trained artificial neural network can modify the one or moreweights, biases, or transfer functions based on the plurality ofnon-pneumatic inputs corresponding to the aircraft operationalparameters.

The method can further include receiving the pneumatic-based air dataoutput value from a pneumatic-based air data system, and identifyingwhether the received pneumatic-based air data output value is determinedto be reliable or whether the received pneumatic-based air data outputvalue is determined to be unreliable. Processing the plurality ofnon-pneumatic inputs through the artificial intelligence network togenerate the air data output value can further include processing thenon-pneumatic inputs and the received pneumatic-based air data outputvalue through the artificial intelligence network to generate the airdata output value when the received pneumatic-based air data outputvalue is determined to be reliable, and processing the non-pneumaticinputs without the received pneumatic-based air data output valuethrough the artificial intelligence network to generate the air dataoutput value when the received pneumatic-based air data output value isdetermined to be unreliable.

The method can further include outputting the air data output value to aconsuming system that determines whether the pneumatic-based air dataoutput value is unreliable based at least in part on the generated airdata value.

The method can further include determining whether the pneumatic-basedair data output value is unreliable.

A synthetic air data system includes at least one processor andcomputer-readable memory. The computer-readable memory is encoded withinstructions that, when executed by the at least one processor, causethe synthetic air data system to receive, over an aircraft datacommunications bus, a plurality of non-pneumatic inputs corresponding toaircraft operational parameters. The computer readable memory is furtherencoded with instructions that, when executed by the at least oneprocessor, cause the synthetic air data system to process the pluralityof non-pneumatic inputs through an artificial intelligence network togenerate an air data output value, and output the air data output valueto a consuming system for use when a pneumatic-based air data outputvalue is determined to be unreliable.

The synthetic air data system of the preceding paragraph can optionallyinclude, additionally and/or alternatively, any one or more of thefollowing features, configurations, operations, and/or additionalcomponents.

The plurality of non-pneumatic inputs can include one or more of anaircraft engine thrust parameter, an aircraft engine throttle setting, aflight control surface position, a flight control surface loading, anaircraft fuel usage rate, an aircraft weight, a landing gear position,an aircraft mass balance, an aircraft acceleration, and an aircraftangular rate.

The computer-readable memory can be encoded with instructions that, whenexecuted by the at least one processor, cause the synthetic air datasystem to process the plurality of non-pneumatic inputs through theartificial intelligence network to generate the air data output valuethat is selected from a group comprising an aircraft calibratedairspeed, an aircraft true airspeed, an aircraft Mach number, anaircraft pressure altitude, an aircraft angle of attack, an aircraftvertical speed, and an aircraft angle of sideslip.

The computer-readable memory can be encoded with instructions that, whenexecuted by the at least one processor, cause the synthetic air datasystem to process the plurality of non-pneumatic inputs through apre-trained artificial neural network having at least one internal layerof neurons that apply one or more weights, biases, or transfer functionsto each of the plurality of non-pneumatic inputs to generate the airdata output value.

The computer-readable memory can be encoded with instructions that, whenexecuted by the at least one processor, cause the synthetic air datasystem to process the plurality of non-pneumatic inputs through thepre-trained artificial intelligence network to generate the air dataoutput value without changing the one or more weights, biases, ortransfer functions.

The computer-readable memory can be encoded with instructions that, whenexecuted by the at least one processor, cause the synthetic air datasystem to process the plurality of non-pneumatic inputs through thepre-trained artificial neural network to generate the air data outputvalue by modifying the one or more weights, biases, or transferfunctions based on the plurality of non-pneumatic inputs correspondingto the aircraft operational parameters.

The computer-readable memory can be further encoded with instructionsthat, when executed by the at least one processor, cause the syntheticair data system to receive the pneumatic-based air data output valuefrom a pneumatic-based air data system, and identify whether thereceived pneumatic-based air data output value is determined to bereliable or whether the received pneumatic-based air data output valueis determined to be unreliable. The computer-readable memory can beencoded with instructions that, when executed by the at least oneprocessor, cause the synthetic air data system to process the pluralityof non-pneumatic inputs through the artificial intelligence network togenerate the air data output value by at least causing the synthetic airdata system to process the non-pneumatic inputs and the receivedpneumatic-based air data output value through the artificialintelligence network to generate the air data output value when thereceived pneumatic-based air data output value is determined to bereliable, and process the non-pneumatic inputs without the receivedpneumatic-based air data output value through the artificialintelligence network to generate the air data output value when thereceived pneumatic-based air data output value is determined to beunreliable.

The computer-readable memory can be further encoded with instructionsthat, when executed by the at least one processor, cause the syntheticair data system to output the air data output value to a consumingsystem that determines whether the pneumatic-based air data output valueis unreliable based at least in part on the generated air data value.

The computer-readable memory can be further encoded with instructionsthat, when executed by the at least one processor, cause the syntheticair data system to determine whether the pneumatic-based air data outputvalue is unreliable.

While the invention has been described with reference to an exemplaryembodiment(s), it will be understood by those skilled in the art thatvarious changes may be made and equivalents may be substituted forelements thereof without departing from the scope of the invention. Inaddition, many modifications may be made to adapt a particular situationor material to the teachings of the invention without departing from theessential scope thereof. Therefore, it is intended that the inventionnot be limited to the particular embodiment(s) disclosed, but that theinvention will include all embodiments falling within the scope of theappended claims.

1. A method comprising: receiving, over an aircraft data communicationsbus, a plurality of non-pneumatic inputs; processing the plurality ofnon-pneumatic inputs through an artificial intelligence network togenerate an air data output value without changing any one or more ofweights, biases, or transfer functions of the artificial intelligencenetwork; and outputting the air data output value to a consuming system.2. The method of claim 1, further comprising: determining whether apneumatic-based air data output value is unreliable; wherein outputtingthe air data output value to the consuming system comprises outputtingthe air data output value to the consuming system in response todetermining that the pneumatic-based air data output value isunreliable.
 3. The method of claim 1, wherein the artificialintelligence network comprises a first artificial intelligence network,wherein the plurality of non-pneumatic inputs comprises a firstplurality of non-pneumatic inputs, and wherein the air data output valuecomprises a first air data output value, the method further comprising:processing a second plurality of non-pneumatic inputs through the secondartificial intelligence network to generate a second air data outputvalue; and outputting the second air data output value to the consumingsystem.
 4. The method of claim 3, wherein the first plurality ofnon-pneumatic inputs is different than the second plurality ofnon-pneumatic inputs.
 5. The method of claim 1, wherein the weights,biases, and transfer functions comprise first weights, biases, andtransfer functions of the artificial intelligence network, and whereinthe air data output value comprises a first air data output value, themethod further comprising: processing the plurality of non-pneumaticinputs through the artificial intelligence network by applying secondweights, biases, and transfer functions to generate a second air dataoutput value; and outputting the second air data output value to theconsuming system.
 6. The method of claim 1, wherein the artificialintelligence network is pre-trained to determine the weights, biases,and transfer functions.
 7. The method of claim 1, wherein the artificialintelligence network comprises an artificial neural network having atleast one internal layer of neurons that apply the weights, biases, andtransfer functions to the plurality of non-pneumatic inputs.
 8. Themethod of claim 1, wherein the plurality of non-pneumatic inputscomprise one or more of an aircraft engine thrust parameter, an aircraftengine throttle setting, a flight control surface position, a flightcontrol surface loading, an aircraft fuel usage rate, an aircraftweight, a landing gear position, an aircraft mass balance, an aircraftacceleration, and an aircraft angular rate.
 9. The method of claim 1,wherein the generated air data output value is selected from a groupcomprising an aircraft calibrated airspeed, an aircraft true airspeed,an aircraft Mach number, an aircraft pressure altitude, an aircraftangle of attack, an aircraft vertical speed, and an aircraft angle ofsideslip.
 10. The method of claim 1, further comprising: receiving apneumatic-based air data output value from a pneumatic-based air datasystem; and identify whether the received pneumatic-based air dataoutput value is determined to be reliable or whether the receivedpneumatic-based air data output value is determined to be unreliable;wherein processing the plurality of non-pneumatic inputs through theartificial intelligence network to generate the air data output valuefurther comprises: processing the non-pneumatic inputs and the receivedpneumatic-based air data output value through the artificialintelligence network to generate the air data output value when thereceived pneumatic-based air data output value is determined to bereliable; and processing the non-pneumatic inputs without the receivedpneumatic-based air data output value through the artificialintelligence network to generate the air data output value when thereceived pneumatic-based air data output value is determined to beunreliable.
 11. An air data system comprising: at least one processor;and non-transitory computer-readable memory encoded with instructionsthat, when executed by the at least one processor, cause the air datasystem to: receive, over an aircraft data communications bus, aplurality of non-pneumatic inputs; process the plurality ofnon-pneumatic inputs through an artificial intelligence network togenerate an air data output value without changing any one or more ofweights, biases, or transfer functions of the artificial intelligencenetwork; and output the air data output value to a consuming system. 12.The air data system of claim 11, wherein the computer-readable memory isfurther encoded with instructions that, when executed by the at leastone processor, cause the air data system to: determine whether apneumatic-based air data output value is unreliable; and output the airdata output value to the consuming system in response to determiningthat the pneumatic-based air data output value is unreliable.
 13. Theair data system of claim 11, wherein the artificial intelligence networkcomprises a first artificial intelligence network, wherein the pluralityof non-pneumatic inputs comprises a first plurality of non-pneumaticinputs, wherein the air data output value comprises a first air dataoutput value, and wherein the computer-readable memory is furtherencoded with instructions that, when executed by the at least oneprocessor, cause the air data system to: process a second plurality ofnon-pneumatic inputs through the second artificial intelligence networkto generate a second air data output value; and output the second airdata output value to the consuming system.
 14. The air data system ofclaim 13, wherein the first plurality of non-pneumatic inputs isdifferent than the second plurality of non-pneumatic inputs.
 15. The airdata system of claim 11, wherein the weights, biases, and transferfunctions comprise first weights, biases, and transfer functions of theartificial intelligence network, wherein the air data output valuecomprises a first air data output value, and wherein thecomputer-readable memory is further encoded with instructions that, whenexecuted by the at least one processor, cause the air data system to:process the plurality of non-pneumatic inputs through the artificialintelligence network by applying second weights, biases, and transferfunctions to generate a second air data output value; and output thesecond air data output value to the consuming system.
 16. The air datasystem of claim 11, wherein the artificial intelligence network ispre-trained to determine the weights, biases, and transfer functions.17. The air data system of claim 11, wherein the artificial intelligencenetwork comprises an artificial neural network having at least oneinternal layer of neurons that apply the weights, biases, and transferfunctions to the plurality of non-pneumatic inputs.
 18. The air datasystem of claim 11, wherein the plurality of non-pneumatic inputscomprise one or more of an aircraft engine thrust parameter, an aircraftengine throttle setting, a flight control surface position, a flightcontrol surface loading, an aircraft fuel usage rate, an aircraftweight, a landing gear position, an aircraft mass balance, an aircraftacceleration, and an aircraft angular rate.
 19. The air data system ofclaim 11, wherein the generated air data output value is selected from agroup comprising an aircraft calibrated airspeed, an aircraft trueairspeed, an aircraft Mach number, an aircraft pressure altitude, anaircraft angle of attack, an aircraft vertical speed, and an aircraftangle of sideslip.
 20. The air data system of claim 11, wherein thecomputer-readable memory is further encoded with instructions that, whenexecuted by the at least one processor, cause the air data system to:receive a pneumatic-based air data output value from a pneumatic-basedair data system; identify whether the received pneumatic-based air dataoutput value is determined to be reliable or whether the receivedpneumatic-based air data output value is determined to be unreliable;process the non-pneumatic inputs and the received pneumatic-based airdata output value through the artificial intelligence network togenerate the air data output value when the received pneumatic-based airdata output value is determined to be reliable; and process thenon-pneumatic inputs without the received pneumatic-based air dataoutput value through the artificial intelligence network to generate theair data output value when the received pneumatic-based air data outputvalue is determined to be unreliable.